A Robust Δ-Safe Hybrid Edge–Vertex–Cell Trajectory Planning Algorithm for Autonomous Navigation in Cancer Research Facilities: Integrating Clearance Preservation, Computational Efficiency, and Geometric Smoothness

Abstract

Carcinogenesis research increasingly relies on advanced technologies, including robotic systems, to enhance experimental precision, reduce human error, and streamline laboratory workflows in controlled environments. Against this backdrop, the deployment of autonomous mobile robots within indoor research facilities demands navigation algorithms that adeptly balance path optimality, computational efficiency, and safety. Traditional path planning methods such as A* and Theta* often generate unnecessarily extended trajectories or compromise obstacle clearance, limiting their applicability in sensitive environments. To address these challenges, this study introduces the Delta-Safe (Δ-Safe) Hybrid Edge–Vertex–Cell Optimal Trajectory Planning Algorithm (HEVC-OTPA), a novel approach that discretises the workspace into uniform grids and integrates hybrid edge, vertex, and cell expansions to ensure robust exploration. Safety is reinforced through a clearance penalty function and Δ-offset termination rules, while redundant nodes are pruned using line-of-sight verification. Cubic Bézier smoothing is applied to enhance geometric feasibility and motion fluidity. Comparative simulations against A* and Theta* across diverse obstacle densities demonstrate that HEVC-OTPA yields path lengths 6–14% shorter, maintains a uniform obstacle clearance of 1.0 m, reduces node expansions by 20–28%, and achieves runtime reductions of 24–28%. These results confirm the algorithm’s ability to deliver improved navigation performance without compromising safety, thereby facilitating faster convergence in complex indoor layouts. By ensuring both computational efficiency and reliable obstacle avoidance, HEVC-OTPA holds significant potential for integration into laboratory automation, clinical material handling, and research facility logistics—key areas where precision, safety, and efficiency are paramount in carcinogenesis-related experimental protocols.